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Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric called Vibration Signal-to-Noise Ratio (VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept of Multi-Signal Consolidation (MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves 3.946% relative amplitude error and 0.02487% relative frequency error in median. Typically, the average amplitude error is only 3.174um when measuring the 100um-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by 69.21% and 97.99% respectively.

Phosphoenolpyruvate/phosphate translocator (PPT) transports phosphoenolpyruvate from the cytosol into the plastid for fatty acid (FA) and other metabolites biosynthesis.This study investigated PPTs' functions in plant growth and seed oil biosynthesis in oilseed crop Brassica napus.We created over-expression and mutant material of BnaPPT1. The plant development, oil content, lipids, metabolites and ultrastructure of seeds were compared to evaluate the gene function.The plastid membrane localized BnaPPT1 was found to be required for normal growth of B. napus. The plants grew slower with yellowish leaves in BnaA08.PPT1 and BnaC08.PPT1 double mutant plants. The results of chloroplast ultrastructural observation and lipid analysis show that BnaPPT1 plays an essential role in membrane lipid synthesis and chloroplast development in leaves, thereby affecting photosynthesis. Moreover, the analysis of primary metabolites and lipids in developing seeds showed that BnaPPT1 could impact seed glycolytic metabolism and lipid level. Knockout of BnaA08.PPT1 and BnaC08.PPT1 resulted in decreasing of the seed oil content by 2.2 to 9.1%, while overexpression of BnaC08.PPT1 significantly promoted the seed oil content by 2.1 to 3.3%.Our results suggest that BnaPPT1 is necessary for plant chloroplast development, and it plays an important role in maintaining plant growth and promoting seed oil accumulation in B. napus.

Android overlay enables one app to draw over other apps by creating an extra View layer atop the host View, which nevertheless can be exploited by malicious apps (malware) to attack users. To combat this threat, prior countermeasures concentrate on restricting the capabilities of overlays at the OS level while sacrificing overlays usability; recently, the overlay mechanism has been substantially updated to prevent a variety of attacks, which however can still be evaded by considerable adversaries. To address these shortcomings, a more pragmatic approach is to enable early detection of overlay-based malware during the app market review process, so that all the capabilities of overlays can stay unchanged. For this purpose, in this paper we first conduct a large-scale comparative study of overlay characteristics in benign and malicious apps, and then implement the OverlayChecker system to automatically detect overlay-based malware for one of the worlds largest Android app stores. In particular, we have made systematic efforts in feature engineering, UI exploration, emulation architecture, and run-time environment, thus maintaining high detection accuracy (97% precision and 97% recall) and short per-app scan time (1.7 minutes) with only two commodity servers, under an intensive workload of 10K newly submitted apps per day.

The prevalence of chronic hepatitis B virus (HBV) infection is high in many countries; however, robust, real-world epidemiological data are lacking. This study describes the prevalence, characteristics, treatment patterns, and long-term clinical outcomes of patients with chronic HBV infection in the US, Germany, and Taiwan.This was a retrospective cohort analysis of three healthcare/insurance claims databases. Individuals were identified as patients with chronic HBV infection if their records contained HBV diagnostic codes from 1 January 2010 to 31 December 2012 (Germany and Taiwan) or 1 January 2013 (USA). Included patients were indexed on 1 January 2013. Patients' demographics, clinical characteristics, and healthcare utilisation were described. Treatment patterns and long-term clinical outcomes over follow-up (to 31 December 2016 or loss to follow-up) were estimated.The prevalence of chronic HBV infection was 0.10%, 0.17%, and 2.39% in the US, Germany, and Taiwan respectively. Prevalence was very low in children, increased rapidly in adulthood, and peaked in 50- 65 year olds before declining in the elderly. More US (16.6%) and German (15.4%) patients were HIV ± HCV coinfected than in Taiwan (4.1%). Baseline clinical characteristics and healthcare utilisation were broadly similar between countries. In total, 19.2%, 11.1%, and 5.9% of non-coinfected adult patients received treatment at index in the US, Germany, and Taiwan, respectively; most frequently with nucleos(t)ide analogue monotherapy (94.4%, 97.2%, 99.8% of treated patients, respectively) and rarely with interferons (0.27%, 1.63%, and 0.06%, respectively). Untreated Taiwanese patients were more likely to remain untreated than elsewhere, and treated Taiwanese patients were less likely to persist with therapy. Generally, the cumulative incidence of long-term clinical outcomes was lowest in Germany.This study provides a contemporary, real-world, intercontinental snapshot of chronic HBV infection. Long-term sequelae occurred in all populations, and treatment levels were low, suggesting an unmet need for (or access to) effective treatments.

Low-Power Wide-Area Networks (LPWANs) has emerged as a promising mechanism to connect billions of low-cost Internet of Things (IoT) devices for wide-area data collection. Long Range (LoRa) [1] is a commercialized and widely deployed wireless technology that facilitates the establishment of LPWANs. As illustrated in Figure 1, a LoRaWAN consists of end nodes, gateways, a network server, and an application server. The collected sensory data (e.g., temperature, humidity) transmitted from the distributed end nodes are relayed by several gateways to the network server.

Delta synchronization (sync) is crucial to the network-level efficiency of cloud storage services, especially when handling large files with small increments. Practical delta sync techniques are, however, only available for PC clients and mobile apps, but not web browsers—the most pervasive and OS-independent access method. To bridge this gap, prior work concentrates on either reversing the delta sync protocol or utilizing the native client, all striving around the tradeoffs among efficiency, applicability, and usability and thus forming an “impossible triangle.” Recently, we note the advent of WebAssembly (WASM) , a portable binary instruction format that is efficient in both encoding size and load time. In principle, the unique advantages of WASM can make web-based applications enjoy near-native runtime speed without significant cloud-side or client-side changes. Thus, we implement a straightforward WASM-based delta sync solution, WASMrsync, finding its quasi-asynchronous working manner and conventional In-situ Separate Memory Allocation greatly increase sync time and memory usage. To address them, we strategically devise sync-async code decoupling and streaming compilation, together with Informed In-place File Construction. The resulting solution, WASMrsync+, achieves comparable sync time as the state-of-the-art (most efficient) solution with nearly only half of memory usage, letting the “impossible triangle” reach a reconciliation.

To shield increasingly severe radiation pollution, ultrathin MXene-based electromagnetic interference (EMI) shielding materials with excellent mechanical properties are urgently demanded in wearable electrical devices or aerospace fields. However, it is still a challenge to fabricate ultrastrong and stiff MXene-based nanocomposites with excellent EMI shielding capacity in a universal and scalable manner. Here, inspired by the natural nacre structure, we propose an efficient superspreading strategy to construct a highly oriented layered "brick-and-mortar" structure using shear-flow-induced alignment of MXene nanosheets at an immiscible hydrogel/oil interface. A continuous and large-area MXene nanocomposite film has been fabricated through a homemade industrial-scale continuous fabrication setup. The prepared MXene nanocomposite films exhibit a tensile strength of 647.6 ± 56 MPa and a Young's modulus of 59.8 ± 6.1 GPa, respectively. These outstanding mechanical properties are attributed to the continuous interphase layer that formed between the well-aligned MXene nanosheets. Moreover, the obtained MXene nanocomposites also show great EMI shielding effectiveness (51.6 dB). We consider that our MXene-based nanocomposite films may be potentially applied as electrical or aerospace devices with superior mechanical properties and high EMI shielding capacity.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolution approaches do not exploit the coding properties of LoRa and thus cannot work well for low SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We theoretically show that NScale introduces

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backscatter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5-199.4 Kbps data rate at various distances, 10.4-52.4x higher than PLoRa.

Nowadays the ultrasonic radar, infrared detection, laser scanning radar or other perception technologies have been widely used in the automatic docking conditions. However, it’s not absolutely applicable in severe weather such as heavy rain and snow days, especially in the operation not allowed to fail, for example, the docking of the future unmanned towbarless towing vehicle (TLTV) with the expensive civil plane. A novel mechanism is proposed for relatively position and posture measuring between the TLTV and the aircraft nose tire during the automatic docking operation. The geometrical relationship between the towing vehicle and the nose tire is obtained by mathematical calculation. The position and posture of the nose tire have been obtained by splice angles and the length of rotatable rods connected with the vehicle during the docking operation.

Motion tracking via Inertial Measurement Units (IMUs) on mobile and wearable devices has attracted significant interest in recent years. High-accuracy IMU-tracking can be applied in various applications, such as indoor navigation, gesture recognition, text input, etc. Many efforts have been devoted to improving IMU-based motion tracking in the last two decades, from early calibration techniques on ships or airplanes, to recent arm motion models used on wearable smart devices. In this paper, we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices. We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.

Cadmium is one of the environmental and occupational pollutants and its potential adverse effects on human health have given rise to substantial concern. Cadmium causes damage to the male reproductive system via induction of germ-cell apoptosis; however, the underlying mechanism of cadmium-induced reproductive toxicity in Leydig cells remains unclear. In this study, twenty mice were divided randomly into four groups and exposed to CdCl2 at concentrations of 0, 0.5, 1.0 and 2.0 mg/kg/day for four consecutive weeks. Testicular injury, abnormal spermatogenesis and apoptosis of Leydig cells were observed in mice. In order to investigate the mechanism of cadmium-induced apoptosis of Leydig cells, a model of mouse Leydig cell line (i.e. TM3 cells) was subjected to treatment with various concentrations of CdCl2. It was found that mitochondrial function was disrupted by cadmium, which also caused a significant elevation in levels of mitochondrial superoxide and cellular ROS. Furthermore, while cadmium increased the expression of mitochondrial fission proteins (DRP1 and FIS1), it reduced the expression of mitochondrial fusion proteins (OPA1 and MFN1). This led to excessive mitochondrial fission, the release of cytochrome c and apoptosis. Conversely, cadmium-induced accumulation of mitochondrial superoxide was decreased by the inhibition of mitochondrial fission through the use of Mdivi-1 (an inhibitor of DRP1). Mdivi-1 also partially prevented the release of cytochrome c from mitochondria to cytosol and attenuated cell apoptosis. Finally, given the accumulation of LC3II and SQSTM1/p62 and the obstruction of Parkin recruitment into damaged mitochondria in TM3 cells, the autophagosome-lysosome fusion was probably inhibited by cadmium. Overall, these findings suggest that cadmium induces apoptosis of mouse Leydig cells via the induction of excessive mitochondrial fission and inhibition of mitophagy.

To investigate the potential associations between household renovation and allergic diseases among preschool children in Wuhan, we conducted a large cross-sectional questionnaire survey among 9455 preschool children aged 3-6 years in Wuhan during November to December 2019. Data on demographics, health status, and home decoration conditions were analysed based on a questionnaire. Compared with tiles/stone/cement floor covering, the use of composite floor significantly increased the risk of diagnosed rhinitis and eczema among children (rhinitis: AOR, 95% CI: 1.36, 1.06-1.73; eczema: AOR, 95% CI: 1.47, 1.17-1.85). Household renovation had significant associations with diagnosed eczema (within 1 year before pregnancy: AOR, 95% CI: 1.34, 1.20-1.50; during pregnancy: AOR, 95% CI: 1.25, 1.08-1.44). This study suggests that use of artificial synthetic materials in home renovation during early childhood and pregnancy may be potential risk factors for childhood asthma, allergic rhinitis, and eczema.

For supported metal catalyst systems, the impact on catalysis originates from the interaction between metal nanoparticles and their support. Metal-support interactions (MSI) can change electronic properties, geometric morphologies, or chemical compositions of metal nanoparticles to make active sites have specific properties and catalytic activities. Fischer-Tropsch synthesis (FTS) is one of the most effective ways to convert cheap non-petroleum-based carbon sources into high value-added chemicals or ultra-clean liquid fuels. In this review, we summarize and classify the impact of MSI on the catalytic activity, selectivity and stability of FTS catalysts. The strategies to tune MSI are introduced in detail, and the recent development of high-efficiency FTS catalysts through the manipulation of SMI strategies has been highlighted. It is emphasized that the active metal sites, which are endowed with special functions by MSI, can change the strength of adsorption bond of adsorbates, consequently controlling the product distribution.

As backscatter-based IoT applications get proliferated, how to exploit backscattered signals for efficient sensing becomes a significant issue. Backscatter-based sensing requires accurate estimation of a backscatter channel (phase and amplitude), which is distorted when multiple signals collide with each other. As a result, the state of the arts is limited to either parallel decoding of collided signal or channel estimation with clean signal. Motivated by the need of high sensing capacity, we in this article present Fireworks, the first approach for channel estimation of parallel backscattered signals. The insight of Fireworks is that although the channel is distorted due to collision, the movements of the ON-OFF Keying modulated signal still preserve the channel properties of the respective tags. By modeling the relationship between the channels and the signal’s moving trajectory in the IQ domain, one can make accurate estimation of the channels directly from the collision. We address practical problems of Fireworks, such as the high computing complexity and the compatibility with the commercial MAC protocol, and implement Fireworks. The results show that Fireworks is able to estimate the channels of up to five tags in parallel. When applied to the tracking application, Fireworks achieves $2\sim 4\times $ improvement in the tracking accuracy, compared with the state-of-the-art approach.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver’s standard. Physical-level CTC means considerable processing complexity at the transmitter, which does not apply to the communication from a low-end transmitter to a high-end receiver. This article proposes LEGO-Fi, which supports the CTC from ZigBee to WiFi and Bluetooth to WiFi. LEGO-Fi is a transmitter-transparent CTC technique, which leaves the processing complexity solely at the receiver side and therefore, makes a critical advance toward bidirectional high-throughput CTC. The key technique inside is cross-demapping, which stems from two key technical insights: 1) a ZigBee/Bluetooth packet leaves distinguishable features when passing the WiFi modules and 2) compared to ZigBee’s/Bluetooth’s simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee/Bluetooth packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6 Kb/s in practice, which is, respectively, $13000\times $ , $1200\times $ , and $7.5\times $ faster than FreeBee, ZigFi, and SymBee, the three existing ZigBee-to-WiFi CTC approaches. For the CTC from Bluetooth to WiFi, LEGO-Fi achieves a throughput of 904.1 Kb/s.

Indoor localization is becoming an emerging requirement in many large shopping malls. Existing indoor localization systems, however, require exhausted system bootstraps and calibration phases. The huge sunk cost usually hinders practical deployment of the indoor localization systems in large shopping malls. In contrast, we observe that floor-plan images of large shopping malls, which highlight the positions of many shops, are widely available in Google Maps, Gaode Maps, Baidu Maps, etc. According to several observed shops, people can localize themselves (called self-localization). However, due to the requirements of geometric sense and space transformation, not all people get used to this way. In this article, we propose EyeLoc, which uses smartphone vision to enable accurate self-localization on a floor-plan image. EyeLoc addresses several challenges, including developing a ubiquitous smartphone vision system, extracting efficient vision clues, and achieving robust measurement error mitigation. We implement EyeLoc in Android and evaluate its performance in emulated environment, two large shopping malls and a semioutdoor large Outlets. The results show that the 90-percentile errors of localization and heading direction are 5.97 m and 20° in 70 000 m2 malls.

Eccentricity detection is a crucial issue for high-speed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and real-time RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. The design of RED further addresses practical issues, such as parameterizing the RUM model, making it robust to dynamic and noisy environments, and considering how the doppler shift may affect our system. We implement RED with COTS RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.6 percent and the detection latency is 0.68 seconds in average.

Self-checkout services in today's retail stores are well received as they set free the labor force of cashiers and shorten conventional checkout lines. However, existing self-checkout options either require customers to scan items one by one, which is troublesome and inefficient, or rely on deployments of massive sensors and cameras together with complex tracking algorithms. On the other hand, RFID-based item-level tagging in retail offers an extraordinary opportunity to enhance current checkout experiences. In this work, we propose Taggo, a lightweight and efficient self-checkout schema utilizing well-deployed RFIDs. Taggo attaches a few anchor tags on the four upper edges of each shopping cart, so as to figure out which cart each item belongs to, through relative positioning among the tagged items and anchor tags without knowing their absolute positions. Specifically, a full-dimension ordering technique is devised to accurately determine the order of tags in each dimension, as well as to address the negative impacts from imperfect measurements in indoor surroundings. Besides, we design a holistic classifying solution based on probabilistic modeling to map each item to the correct cart that carries it. We have implemented Taggo with commercial RFID devices and evaluated it extensively in our lab environment. On average, Taggo achieves 90% ordering accuracy in real-time, eventually producing 95% classifying accuracy.

With the development of the Internet of Things (IoT), many kinds of wireless signals (e.g., Wi-Fi, LoRa, RFID) are filling our living and working spaces nowadays. Beyond communication, wireless signals can sense the status of surrounding objects, known as wireless sensing , with their reflection, scattering, and refraction while propagating in space. In the last decade, many sophisticated wireless sensing techniques and systems were widely studied for various applications (e.g., gesture recognition, localization, and object imaging). Recently, deep Artificial Intelligence (AI), also known as Deep Learning (DL), has shown great success in computer vision. And some works have initially proved that deep AI can benefit wireless sensing as well, leading to a brand-new step toward ubiquitous sensing. In this survey, we focus on the evolution of wireless sensing enhanced by deep AI techniques. We first present a general workflow of Wireless Sensing Systems (WSSs) which consists of signal pre-processing, high-level feature, and sensing model formulation. For each module, existing deep AI-based techniques are summarized, further compared with traditional approaches. Then, we provide a view of issues and challenges induced by combining deep AI and wireless sensing together. Finally, we discuss the future trends of deep AI to enable ubiquitous wireless sensing.

Bile acid: sodium symporter family protein 2 (BASS2) is a sodium-dependent pyruvate transporter, which transports pyruvate from cytosol into plastid in plants. In this study, we investigated the function of chloroplast envelope membrane-localized BnaBASS2 in seed metabolism and seed oil accumulation of Brassica napus (B. napus). Four BASS2 genes were identified in the genome of B. napus. BnaA05.BASS2 was overexpressed while BnaA05.BASS2 and BnaC04.BASS2-1 were mutated by CRISPR in B. napus. Metabolite analysis revealed that the manipulation of BnaBASS2 caused significant changes in glycolysis-, fatty acid synthesis-, and energy-related metabolites in the chloroplasts of 31 day-after-flowering (DAF) seeds. The analysis of fatty acids and lipids in developing seeds showed that BnaBASS2 could affect lipid metabolism and oil accumulation in developing seeds. Moreover, the overexpression (OE) of BnaA05.BASS2 could promote the expression level of multiple genes involved in the synthesis of oil and the formation of oil body during seed development. Disruption of BnaA05.BASS2 and BnaC04.BASS2-1 resulted in decreasing the seed oil content (SOC) by 2.8%-5.0%, while OE of BnaA05.BASS2 significantly promoted the SOC by 1.4%-3.4%. Together, our results suggest that BnaBASS2 is a potential target gene for breeding B. napus with high SOC.

Quercetin (QCT) has antioxidant, anti-inflammatory, anti-tumor and other important pharmacological activities, but the poor water solubility limits its application. In this work, the amphiphilic dextran (ADEX) was prepared by grafting L-cysteine and octadecylamine onto carboxymethyl dextran with the grafting rate of 21.29 % and 19.35 %. Then, the QCT-loaded nanomicelles (QNMs) were prepared by using ADEX as wall material and the QCT as core material via ultrasonic self-assembly method. The particle size and zeta potential of QNMs were 372 nm and 31.4 mV. Under simulated gastric and simulated intestinal fluids, the cumulative release QNMs were 37.54 % and 52.13 % within 180 min, and the QNMs showed better stability in simulated gastric fluid. The QNMs showed significantly better PTIO, OH and O

Online traveling community is initiated by companies, but its survival is inextricably linked to consumer citizenship behavior (e.g., out-group recommendation, in-group helping, and inward response). The majority of researches have investigated consumer behavior of brand community such as consumer satisfaction, brand loyalty, and purchase intention. A few scholars try to explore consumer behaviors beyond the purchase, like participation, which was concerned as the value co-creation. However, the value co-creation of the community should depend on consumers’ citizenship behaviors instead of their pure participation. Therefore, this study empirically examines the effect of consumer interaction on consumer psychology and citizenship behaviors of the online travel community. The findings demonstrated that consumer interaction facilitated participants’ self-identity and their perceived social support, which enhanced their community identification and thus their citizenship behaviors. Furthermore, the motivation of participation plays a moderator in this process. Specifically, symbolic motivation moderates the relationship between consumer interaction and their self-identity, while utilitarian motivation moderates the effect of consumer interaction on their perceived social support. These findings contributed to the intervention of consumer citizenship behavior in online traveling community and provide insights into the management of the online travel community from the perspective of the value co-creation.

Chitosan (CS) is a natural marine polysaccharide with good biocompatibility and biodegradability. But its poor water solubility and antibacterial activity limit its application in fruits preservation. In this study, based on the advantage of quaternary phosphonium salt (QP) and salicylic acid (SA) with good antibacterial activities and different antibacterial mechanisms, a novel antibacterial coating material was synthesized by grafting QP and SA onto CS. With the grafting of SA and QP onto CS, not only the crystallinity of CS molecules decreased and the water solubility improved, but also the antibacterial activity of CS-QP-SA against Escherichia coli and Staphylococcus aureus, and Colletotrichum gloeosporioides (anthracnose) improved by the synergistic effect of QP and SA. After 20 days storage, the mango fruits treated by CS-QP-SA had a weight loss rate of 12.86 %, the fruit decay incidence was 52.00 ± 1.70 %. Hence, the CS-QP-SA films effectively extending the storage time of mango fruits to a certain extent. The results of this study indicated that CS-QP-SA might be a promising preservative for fruits and vegetables.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. They are inherently difficult to achieve both high accuracy and low delay. In this paper, we present Vernier, an efficient and accurate acoustic tracking method based on commodity mobile devices. We design a new approach to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance with COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Experimental results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3× improvement to the previous phase based approaches and 10× to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, $3G$ HSDPA data set from the Internet, as well as the $4G$ /LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in dash.js at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, in this article, we propose OmniTrack, an orientation-aware RFID tracking approach. Our study presents a generic model that discribes the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tag's orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeter-level location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.

Chad Moretz,1 Ashley L Cole,2 George Mu,2 Benjamin Wu,1 Amy Guisinger,1 Yunhao Liu,2 Beth Hahn,1 Lee Baylis3 1US Value Evidence & Outcomes, GlaxoSmithKline, Research Triangle Park, Durham, NC, USA; 2VEO Data, Methods, and Analytics, GlaxoSmithKline, Collegeville, PA, USA; 3US Medical Affairs, GlaxoSmithKline, Research Triangle Park, Durham, NC, USACorrespondence: Beth HahnGlaxoSmithKline, 5 Moore Drive, Research Triangle Park, Durham, NC, USATel +1 919 274 0660Email beth.a.hahn@gsk.comBackground: Adherence to inhaled maintenance therapy is critical to managing chronic obstructive pulmonary disease (COPD), while increasing rescue medication usage may indicate worsening symptoms. This study evaluated adherence and rescue medication use in patients with COPD without a history of exacerbation who initiated combination therapy with budesonide/formoterol (B/F) or umeclidinium/vilanterol (UMEC/VI).Methods: Retrospective observational study of commercially insured and Medicare Advantage with Part D enrollees who initiated UMEC/VI or B/F between January 1, 2014 and December 31, 2017 (earliest fill defined as index date). Eligibility criteria included age ≥ 40 years, 12 months continuous enrollment pre- and post-index, ≥ 1 pre-index COPD diagnosis, no pre-index asthma diagnosis, COPD-related exacerbations, or medication fills containing inhaled corticosteroids, long-acting β2-agonists, or long-acting muscarinic antagonists. Inverse probability of treatment weighting (IPTW) was used to balance treatment groups on potential confounders. Medication adherence (primary endpoint) was evaluated by the proportion of days covered (PDC). Rescue medication use (secondary endpoint) was standardized to canister equivalents (1 metered dose inhaler [200 puffs] or ∼ 100 nebulized doses of short-acting β2-agonist- and/or short-acting muscarinic agonist-containing medication).Results: After IPTW, covariates were balanced between cohorts (UMEC/VI: N=4082; B/F: N=9529). UMEC/VI initiators had a significantly greater mean PDC (UMEC/VI: 0.47 [0.33]; B/F: 0.38 [0.30]; P< 0.001) and significantly higher rates of adherence (PDC≥ 0.80) than B/F initiators (UMEC/VI: n=1004 [25%], B/F: n=1391 [15%]; relative risk: 1.68, 95% CI: 1.57, 1.81; P< 0.001). In the year following initiation, UMEC/VI initiators filled significantly fewer rescue medication canister equivalents than B/F initiators (predicted mean [95% CI]: 1.78 [1.69, 1.88] vs 2.15 [2.08, 2.23]; mean difference [95% CI]: − 0.37 [− 0.50, − 0.24]; P< 0.001), corresponding to 17% less (estimated) rescue medication use (incidence rate ratio [95% CI]: 0.83 [0.78, 0.88]).Conclusion: Among non-exacerbating patients with COPD initiating dual therapy, UMEC/VI demonstrated improved adherence and reduced rescue medication use compared with B/F.Keywords: COPD, LAMA/LABA, ICS/LABA, medication adherence, rescue medication use